{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乐学偶得版权所有 lexueoude.com 公众号：乐学Fintech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import talib\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# close=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]\n",
    "# # 列表\n",
    "# close\n",
    "# # talib需要pass进去array（数组）\n",
    "# type(close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
       "       14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n",
       "       27., 28., 29., 30.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close=np.array([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0])\n",
    "close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,\n",
       "        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,\n",
       "        nan,  nan,  nan,  nan,  nan,  nan,  nan, 15.5])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output=talib.SMA(close)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((1+30)*30/2)/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import talib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pro=ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190628</td>\n",
       "      <td>13.73</td>\n",
       "      <td>13.79</td>\n",
       "      <td>13.58</td>\n",
       "      <td>13.78</td>\n",
       "      <td>13.71</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.5106</td>\n",
       "      <td>498093.69</td>\n",
       "      <td>682679.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190627</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.85</td>\n",
       "      <td>13.45</td>\n",
       "      <td>13.71</td>\n",
       "      <td>13.37</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5430</td>\n",
       "      <td>925074.94</td>\n",
       "      <td>1270042.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190626</td>\n",
       "      <td>13.27</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.19</td>\n",
       "      <td>13.37</td>\n",
       "      <td>13.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.6020</td>\n",
       "      <td>546504.76</td>\n",
       "      <td>731207.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190625</td>\n",
       "      <td>13.72</td>\n",
       "      <td>13.72</td>\n",
       "      <td>13.07</td>\n",
       "      <td>13.43</td>\n",
       "      <td>13.69</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-1.8992</td>\n",
       "      <td>1469227.07</td>\n",
       "      <td>1954855.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000001.SZ</td>\n",
       "      <td>20190624</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.83</td>\n",
       "      <td>13.61</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.64</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3666</td>\n",
       "      <td>659572.85</td>\n",
       "      <td>904433.349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0  000001.SZ   20190628  13.73  13.79  13.58  13.78      13.71    0.07   \n",
       "1  000001.SZ   20190627  13.50  13.85  13.45  13.71      13.37    0.34   \n",
       "2  000001.SZ   20190626  13.27  13.50  13.19  13.37      13.29    0.08   \n",
       "3  000001.SZ   20190625  13.72  13.72  13.07  13.43      13.69   -0.26   \n",
       "4  000001.SZ   20190624  13.69  13.83  13.61  13.69      13.64    0.05   \n",
       "\n",
       "   pct_chg         vol       amount  \n",
       "0   0.5106   498093.69   682679.970  \n",
       "1   2.5430   925074.94  1270042.461  \n",
       "2   0.6020   546504.76   731207.282  \n",
       "3  -1.8992  1469227.07  1954855.785  \n",
       "4   0.3666   659572.85   904433.349  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pro.daily(ts_code=\"000001.SZ\",start_date=\"20180101\",end_date=\"20190630\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351    11.7579\n",
       "352    11.7927\n",
       "353    11.8272\n",
       "354    11.8613\n",
       "355    11.8917\n",
       "356    11.9201\n",
       "357    11.9494\n",
       "358    11.9805\n",
       "359    12.0112\n",
       "360    12.0455\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Python_Rolling_df=df[\"close\"].rolling(100).mean()\n",
    "Python_Rolling_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Python_Rolling_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close=[x for x in df[\"close\"]]\n",
    "type(close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "talib_df=talib.MA(np.array(close),timeperiod=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan,     nan,     nan,     nan,     nan,     nan,     nan,\n",
       "           nan, 12.6855, 12.6577, 12.6258, 12.5956, 12.5641, 12.5306,\n",
       "       12.4985, 12.463 , 12.4371, 12.4115, 12.3859, 12.363 , 12.3381,\n",
       "       12.3118, 12.2819, 12.2559, 12.2342, 12.2073, 12.1807, 12.1555,\n",
       "       12.1265, 12.0973, 12.0667, 12.036 , 12.0068, 11.9804, 11.9569,\n",
       "       11.9341, 11.9114, 11.8893, 11.8688, 11.8427, 11.8158, 11.7926,\n",
       "       11.7724, 11.7481, 11.731 , 11.7109, 11.6873, 11.6622, 11.6257,\n",
       "       11.5875, 11.5514, 11.5135, 11.4723, 11.4371, 11.4017, 11.3601,\n",
       "       11.3252, 11.2874, 11.2478, 11.2153, 11.1865, 11.1567, 11.1249,\n",
       "       11.0957, 11.0642, 11.034 , 11.0088, 10.9861, 10.9626, 10.9435,\n",
       "       10.9303, 10.914 , 10.9048, 10.8966, 10.8811, 10.8626, 10.8466,\n",
       "       10.8263, 10.7981, 10.7764, 10.7558, 10.7332, 10.7126, 10.688 ,\n",
       "       10.6695, 10.6477, 10.6214, 10.6013, 10.5788, 10.5583, 10.5402,\n",
       "       10.5229, 10.5032, 10.4801, 10.4655, 10.4488, 10.4331, 10.42  ,\n",
       "       10.4052, 10.3948, 10.3811, 10.3674, 10.3552, 10.3436, 10.3359,\n",
       "       10.3268, 10.3186, 10.3098, 10.3031, 10.296 , 10.2951, 10.2919,\n",
       "       10.2827, 10.2722, 10.2602, 10.2477, 10.231 , 10.2164, 10.2032,\n",
       "       10.1912, 10.1807, 10.1736, 10.1699, 10.163 , 10.1572, 10.1538,\n",
       "       10.151 , 10.1466, 10.1453, 10.1465, 10.147 , 10.1453, 10.1433,\n",
       "       10.14  , 10.1346, 10.1276, 10.1158, 10.1014, 10.0845, 10.0693,\n",
       "       10.0543, 10.0414, 10.0274, 10.0127,  9.998 ,  9.9824,  9.9631,\n",
       "        9.9455,  9.9296,  9.9135,  9.8978,  9.8853,  9.8713,  9.8567,\n",
       "        9.8442,  9.8331,  9.8231,  9.816 ,  9.8089,  9.8032,  9.7995,\n",
       "        9.7946,  9.7886,  9.7803,  9.7726,  9.7654,  9.7599,  9.7504,\n",
       "        9.7447,  9.7383,  9.7312,  9.7255,  9.7145,  9.7054,  9.7009,\n",
       "        9.6984,  9.693 ,  9.6919,  9.6996,  9.7058,  9.7117,  9.7188,\n",
       "        9.7248,  9.7374,  9.7447,  9.7492,  9.7548,  9.754 ,  9.7567,\n",
       "        9.7577,  9.759 ,  9.7598,  9.7663,  9.7724,  9.7858,  9.8057,\n",
       "        9.8259,  9.842 ,  9.8567,  9.8723,  9.8885,  9.9005,  9.9118,\n",
       "        9.927 ,  9.9379,  9.9543,  9.9672,  9.9762,  9.9822,  9.9849,\n",
       "        9.9877,  9.9964, 10.0133, 10.0293, 10.0464, 10.0657, 10.091 ,\n",
       "       10.1198, 10.1509, 10.1791, 10.2069, 10.231 , 10.2552, 10.2839,\n",
       "       10.3124, 10.3433, 10.3751, 10.4068, 10.4358, 10.4626, 10.4873,\n",
       "       10.5143, 10.5422, 10.5689, 10.5977, 10.6298, 10.6648, 10.7021,\n",
       "       10.7351, 10.7673, 10.7972, 10.8253, 10.8619, 10.9033, 10.9535,\n",
       "       11.0087, 11.0626, 11.1169, 11.1713, 11.2211, 11.2724, 11.322 ,\n",
       "       11.3748, 11.4303, 11.4832, 11.533 , 11.5825, 11.6311, 11.6743,\n",
       "       11.7176, 11.7579, 11.7927, 11.8272, 11.8613, 11.8917, 11.9201,\n",
       "       11.9494, 11.9805, 12.0112, 12.0455])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talib_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elasped time : 1597556 ns\n"
     ]
    }
   ],
   "source": [
    "from time import perf_counter_ns\n",
    "\n",
    "t1_start=perf_counter_ns()\n",
    "\n",
    "Python_Rolling_df=df[\"close\"].rolling(100).mean()\n",
    "# Python_Rolling_df.tail(10)\n",
    "\n",
    "\n",
    "t1_stop=perf_counter_ns()\n",
    "elapsed_time=t1_stop-t1_start\n",
    "print(\"Elasped time :\" , elapsed_time,\"ns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elasped time : 177720 ns\n"
     ]
    }
   ],
   "source": [
    "from time import perf_counter_ns\n",
    "\n",
    "t2_start=perf_counter_ns()\n",
    "\n",
    "talib_df=talib.MA(np.array(close),timeperiod=100)\n",
    "\n",
    "t2_stop=perf_counter_ns()\n",
    "elapsed_time_2=t2_stop-t2_start\n",
    "print(\"Elasped time :\" , elapsed_time_2,\"ns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.989173981544003"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elapsed_time/elapsed_time_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "symbol=\"000001.SZ\"\n",
    "df=df[\"close\"]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ax = df.plot(title='{} Price and BB'.format(symbol))\n",
    "# ax.fill_between(df.index, lower_band['lower'], upper_band['upper'], color='#ADCCFF', alpha='0.4')\n",
    "# ax.set_xlabel('date')\n",
    "# ax.set_ylabel('SMA and BB')\n",
    "# ax.grid()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          NaN\n",
       "1          NaN\n",
       "2          NaN\n",
       "3          NaN\n",
       "4          NaN\n",
       "5          NaN\n",
       "6          NaN\n",
       "7          NaN\n",
       "8          NaN\n",
       "9          NaN\n",
       "10         NaN\n",
       "11         NaN\n",
       "12         NaN\n",
       "13         NaN\n",
       "14         NaN\n",
       "15         NaN\n",
       "16         NaN\n",
       "17         NaN\n",
       "18         NaN\n",
       "19     12.8210\n",
       "20     12.7430\n",
       "21     12.6775\n",
       "22     12.6335\n",
       "23     12.5805\n",
       "24     12.5135\n",
       "25     12.4460\n",
       "26     12.3760\n",
       "27     12.3505\n",
       "28     12.3295\n",
       "29     12.3180\n",
       "        ...   \n",
       "331    12.0605\n",
       "332    12.0630\n",
       "333    12.1045\n",
       "334    12.1545\n",
       "335    12.2535\n",
       "336    12.3795\n",
       "337    12.4775\n",
       "338    12.5735\n",
       "339    12.6735\n",
       "340    12.7510\n",
       "341    12.8450\n",
       "342    12.9500\n",
       "343    13.0580\n",
       "344    13.1875\n",
       "345    13.3060\n",
       "346    13.3965\n",
       "347    13.5060\n",
       "348    13.6190\n",
       "349    13.7305\n",
       "350    13.8435\n",
       "351    13.9675\n",
       "352    14.0605\n",
       "353    14.1035\n",
       "354    14.1310\n",
       "355    14.0850\n",
       "356    14.0055\n",
       "357    13.9680\n",
       "358    13.9290\n",
       "359    13.8930\n",
       "360    13.8955\n",
       "Name: close, Length: 361, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate Simple Moving Average with 20 days window\n",
    "sma = df.rolling(window=20).mean()\n",
    "sma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2           NaN\n",
       "3           NaN\n",
       "4           NaN\n",
       "5           NaN\n",
       "6           NaN\n",
       "7           NaN\n",
       "8           NaN\n",
       "9           NaN\n",
       "10          NaN\n",
       "11          NaN\n",
       "12          NaN\n",
       "13          NaN\n",
       "14          NaN\n",
       "15          NaN\n",
       "16          NaN\n",
       "17          NaN\n",
       "18          NaN\n",
       "19     0.691161\n",
       "20     0.664760\n",
       "21     0.627987\n",
       "22     0.607404\n",
       "23     0.579868\n",
       "24     0.519162\n",
       "25     0.447853\n",
       "26     0.314700\n",
       "27     0.273467\n",
       "28     0.252451\n",
       "29     0.241107\n",
       "         ...   \n",
       "331    0.266369\n",
       "332    0.262420\n",
       "333    0.269199\n",
       "334    0.321010\n",
       "335    0.520610\n",
       "336    0.727508\n",
       "337    0.813400\n",
       "338    0.878445\n",
       "339    0.928146\n",
       "340    0.942343\n",
       "341    0.942553\n",
       "342    0.954496\n",
       "343    0.968214\n",
       "344    0.999015\n",
       "345    1.025903\n",
       "346    1.042826\n",
       "347    1.070506\n",
       "348    1.073557\n",
       "349    1.010510\n",
       "350    0.922259\n",
       "351    0.777004\n",
       "352    0.575157\n",
       "353    0.479729\n",
       "354    0.420412\n",
       "355    0.481407\n",
       "356    0.529463\n",
       "357    0.552217\n",
       "358    0.574694\n",
       "359    0.589086\n",
       "360    0.588106\n",
       "Name: close, Length: 361, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the standar deviation\n",
    "rstd = df.rolling(window=20).std()\n",
    "rstd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>15.064426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>15.072433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>15.078387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>15.071172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>15.071711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         upper\n",
       "356  15.064426\n",
       "357  15.072433\n",
       "358  15.078387\n",
       "359  15.071172\n",
       "360  15.071711"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_band = sma + 2 * rstd\n",
    "upper_band=upper_band.to_frame()\n",
    "upper_band=upper_band.rename({'close': 'upper'}, axis=1) \n",
    "upper_band.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>12.946574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>12.863567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>12.779613</td>\n",
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       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>12.714828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>12.719289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lower\n",
       "356  12.946574\n",
       "357  12.863567\n",
       "358  12.779613\n",
       "359  12.714828\n",
       "360  12.719289"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_band = sma - 2 * rstd\n",
    "lower_band=lower_band.to_frame()\n",
    "lower_band=lower_band.rename({'close': 'lower'}, axis=1) \n",
    "lower_band.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>upper</th>\n",
       "      <th>lower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.18</td>\n",
       "      <td>14.203323</td>\n",
       "      <td>11.438677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.22</td>\n",
       "      <td>14.072521</td>\n",
       "      <td>11.413479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.40</td>\n",
       "      <td>13.933473</td>\n",
       "      <td>11.421527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12.49</td>\n",
       "      <td>13.848308</td>\n",
       "      <td>11.418692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.37</td>\n",
       "      <td>13.740236</td>\n",
       "      <td>11.420764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12.35</td>\n",
       "      <td>13.551824</td>\n",
       "      <td>11.475176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12.29</td>\n",
       "      <td>13.341707</td>\n",
       "      <td>11.550293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12.40</td>\n",
       "      <td>13.005399</td>\n",
       "      <td>11.746601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.56</td>\n",
       "      <td>12.897433</td>\n",
       "      <td>11.803567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12.38</td>\n",
       "      <td>12.834401</td>\n",
       "      <td>11.824599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12.44</td>\n",
       "      <td>12.800214</td>\n",
       "      <td>11.835786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>11.72</td>\n",
       "      <td>12.593238</td>\n",
       "      <td>11.527762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>11.69</td>\n",
       "      <td>12.587840</td>\n",
       "      <td>11.538160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>12.54</td>\n",
       "      <td>12.642898</td>\n",
       "      <td>11.566102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>12.92</td>\n",
       "      <td>12.796519</td>\n",
       "      <td>11.512481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>14.00</td>\n",
       "      <td>13.294719</td>\n",
       "      <td>11.212281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>14.55</td>\n",
       "      <td>13.834516</td>\n",
       "      <td>10.924484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>14.05</td>\n",
       "      <td>14.104300</td>\n",
       "      <td>10.850700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>14.03</td>\n",
       "      <td>14.330390</td>\n",
       "      <td>10.816610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>14.05</td>\n",
       "      <td>14.529793</td>\n",
       "      <td>10.817207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>13.65</td>\n",
       "      <td>14.635685</td>\n",
       "      <td>10.866315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>13.74</td>\n",
       "      <td>14.730105</td>\n",
       "      <td>10.959895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>14.05</td>\n",
       "      <td>14.858993</td>\n",
       "      <td>11.041007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>14.20</td>\n",
       "      <td>14.994428</td>\n",
       "      <td>11.121572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>14.64</td>\n",
       "      <td>15.185529</td>\n",
       "      <td>11.189471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>14.65</td>\n",
       "      <td>15.357807</td>\n",
       "      <td>11.254193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>14.44</td>\n",
       "      <td>15.482153</td>\n",
       "      <td>11.310847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>14.80</td>\n",
       "      <td>15.647012</td>\n",
       "      <td>11.364988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>14.72</td>\n",
       "      <td>15.766115</td>\n",
       "      <td>11.471885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>14.23</td>\n",
       "      <td>15.751521</td>\n",
       "      <td>11.709479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>14.20</td>\n",
       "      <td>15.688017</td>\n",
       "      <td>11.998983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>14.20</td>\n",
       "      <td>15.521508</td>\n",
       "      <td>12.413492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>13.55</td>\n",
       "      <td>15.210813</td>\n",
       "      <td>12.910187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>13.40</td>\n",
       "      <td>15.062958</td>\n",
       "      <td>13.144042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>13.47</td>\n",
       "      <td>14.971824</td>\n",
       "      <td>13.290176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>13.08</td>\n",
       "      <td>15.047814</td>\n",
       "      <td>13.122186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>12.96</td>\n",
       "      <td>15.064426</td>\n",
       "      <td>12.946574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>13.30</td>\n",
       "      <td>15.072433</td>\n",
       "      <td>12.863567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>13.25</td>\n",
       "      <td>15.078387</td>\n",
       "      <td>12.779613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>13.33</td>\n",
       "      <td>15.071172</td>\n",
       "      <td>12.714828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>13.70</td>\n",
       "      <td>15.071711</td>\n",
       "      <td>12.719289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>361 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     close      upper      lower\n",
       "0    13.78        NaN        NaN\n",
       "1    13.71        NaN        NaN\n",
       "2    13.37        NaN        NaN\n",
       "3    13.43        NaN        NaN\n",
       "4    13.69        NaN        NaN\n",
       "5    13.64        NaN        NaN\n",
       "6    13.80        NaN        NaN\n",
       "7    13.07        NaN        NaN\n",
       "8    12.80        NaN        NaN\n",
       "9    12.67        NaN        NaN\n",
       "10   12.49        NaN        NaN\n",
       "11   12.59        NaN        NaN\n",
       "12   12.57        NaN        NaN\n",
       "13   12.65        NaN        NaN\n",
       "14   12.34        NaN        NaN\n",
       "15   11.92        NaN        NaN\n",
       "16   11.97        NaN        NaN\n",
       "17   11.85        NaN        NaN\n",
       "18   11.90        NaN        NaN\n",
       "19   12.18  14.203323  11.438677\n",
       "20   12.22  14.072521  11.413479\n",
       "21   12.40  13.933473  11.421527\n",
       "22   12.49  13.848308  11.418692\n",
       "23   12.37  13.740236  11.420764\n",
       "24   12.35  13.551824  11.475176\n",
       "25   12.29  13.341707  11.550293\n",
       "26   12.40  13.005399  11.746601\n",
       "27   12.56  12.897433  11.803567\n",
       "28   12.38  12.834401  11.824599\n",
       "29   12.44  12.800214  11.835786\n",
       "..     ...        ...        ...\n",
       "331  11.72  12.593238  11.527762\n",
       "332  11.69  12.587840  11.538160\n",
       "333  12.54  12.642898  11.566102\n",
       "334  12.92  12.796519  11.512481\n",
       "335  14.00  13.294719  11.212281\n",
       "336  14.55  13.834516  10.924484\n",
       "337  14.05  14.104300  10.850700\n",
       "338  14.03  14.330390  10.816610\n",
       "339  14.05  14.529793  10.817207\n",
       "340  13.65  14.635685  10.866315\n",
       "341  13.74  14.730105  10.959895\n",
       "342  14.05  14.858993  11.041007\n",
       "343  14.20  14.994428  11.121572\n",
       "344  14.64  15.185529  11.189471\n",
       "345  14.65  15.357807  11.254193\n",
       "346  14.44  15.482153  11.310847\n",
       "347  14.80  15.647012  11.364988\n",
       "348  14.72  15.766115  11.471885\n",
       "349  14.23  15.751521  11.709479\n",
       "350  14.20  15.688017  11.998983\n",
       "351  14.20  15.521508  12.413492\n",
       "352  13.55  15.210813  12.910187\n",
       "353  13.40  15.062958  13.144042\n",
       "354  13.47  14.971824  13.290176\n",
       "355  13.08  15.047814  13.122186\n",
       "356  12.96  15.064426  12.946574\n",
       "357  13.30  15.072433  12.863567\n",
       "358  13.25  15.078387  12.779613\n",
       "359  13.33  15.071172  12.714828\n",
       "360  13.70  15.071711  12.719289\n",
       "\n",
       "[361 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df,upper_band, lower_band], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(title='{} Price and BB'.format(symbol))\n",
    "ax.fill_between(df.index, lower_band['lower'], upper_band['upper'], color='#ADCCFF', alpha='0.8')\n",
    "ax.set_xlabel('date')\n",
    "ax.set_ylabel('SMA and BB')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
      "trade_date                                                                      \n",
      "2019-06-28  000001.SZ  13.73  13.79  13.58  13.78      13.71    0.07   0.5106   \n",
      "2019-06-27  000001.SZ  13.50  13.85  13.45  13.71      13.37    0.34   2.5430   \n",
      "2019-06-26  000001.SZ  13.27  13.50  13.19  13.37      13.29    0.08   0.6020   \n",
      "2019-06-25  000001.SZ  13.72  13.72  13.07  13.43      13.69   -0.26  -1.8992   \n",
      "2019-06-24  000001.SZ  13.69  13.83  13.61  13.69      13.64    0.05   0.3666   \n",
      "\n",
      "                   vol       amount  \n",
      "trade_date                           \n",
      "2019-06-28   498093.69   682679.970  \n",
      "2019-06-27   925074.94  1270042.461  \n",
      "2019-06-26   546504.76   731207.282  \n",
      "2019-06-25  1469227.07  1954855.785  \n",
      "2019-06-24   659572.85   904433.349  \n",
      "            close      upper      lower\n",
      "trade_date                             \n",
      "2019-05-31  12.18  14.203323  11.438677\n",
      "2019-05-30  12.22  14.072521  11.413479\n",
      "2019-05-29  12.40  13.933473  11.421527\n",
      "2019-05-28  12.49  13.848308  11.418692\n",
      "2019-05-27  12.37  13.740236  11.420764\n",
      "2019-05-24  12.35  13.551824  11.475176\n",
      "2019-05-23  12.29  13.341707  11.550293\n",
      "2019-05-22  12.40  13.005399  11.746601\n",
      "2019-05-21  12.56  12.897433  11.803567\n",
      "2019-05-20  12.38  12.834401  11.824599\n",
      "2019-05-17  12.44  12.800214  11.835786\n",
      "2019-05-16  12.85  12.869406  11.802594\n",
      "2019-05-15  12.92  12.936961  11.768039\n",
      "2019-05-14  12.49  12.927765  11.769235\n",
      "2019-05-13  12.30  12.892798  11.769202\n",
      "2019-05-10  12.68  12.931117  11.764883\n",
      "2019-05-09  12.16  12.915243  11.804757\n",
      "2019-05-08  12.60  12.924625  11.858375\n",
      "2019-05-07  12.95  12.971307  11.921693\n",
      "2019-05-06  12.87  12.985306  12.004694\n",
      "2019-04-30  13.85  13.337898  11.819102\n",
      "2019-04-29  14.10  13.672392  11.672608\n",
      "2019-04-26  13.79  13.849574  11.634426\n",
      "2019-04-25  14.13  14.085201  11.562799\n",
      "2019-04-24  14.44  14.359949  11.495051\n",
      "2019-04-23  14.07  14.505264  11.521736\n",
      "2019-04-22  14.15  14.639688  11.573312\n",
      "2019-04-19  14.73  14.879300  11.566700\n",
      "2019-04-18  14.34  15.009089  11.614911\n",
      "2019-04-17  14.35  15.108505  11.712495\n",
      "...           ...        ...        ...\n",
      "2017-07-12  10.34  11.312729  10.493271\n",
      "2017-07-11  10.25  11.359349  10.366651\n",
      "2017-07-10   9.59  11.542598  10.042402\n",
      "2017-07-07   9.47  11.640472   9.774528\n",
      "2017-07-06   9.40  11.715023   9.538977\n",
      "2017-07-05   9.37  11.731943   9.344057\n",
      "2017-07-04   9.34  11.735253   9.170747\n",
      "2017-07-03   9.40  11.749910   9.029090\n",
      "2017-06-30   9.39  11.741853   8.902147\n",
      "2017-06-29   9.43  11.731721   8.796279\n",
      "2017-06-28   9.43  11.693450   8.703550\n",
      "2017-06-27   9.36  11.606244   8.626756\n",
      "2017-06-26   9.30  11.512098   8.555902\n",
      "2017-06-23   9.25  11.412001   8.491999\n",
      "2017-06-22   9.25  11.275269   8.456731\n",
      "2017-06-21   9.15  11.087690   8.450310\n",
      "2017-06-20   9.12  10.873725   8.471275\n",
      "2017-06-19   9.13  10.685218   8.491782\n",
      "2017-06-16   9.02  10.428050   8.560950\n",
      "2017-06-15   9.04  10.081725   8.721275\n",
      "2017-06-14   9.08   9.869845   8.807155\n",
      "2017-06-13   9.12   9.604529   8.959471\n",
      "2017-06-12   9.11   9.554407   8.961593\n",
      "2017-06-09   9.15   9.524440   8.959560\n",
      "2017-06-08   9.13   9.504888   8.952112\n",
      "2017-06-07   9.13   9.487815   8.945185\n",
      "2017-06-06   9.04   9.477202   8.925798\n",
      "2017-06-05   9.03   9.452198   8.913802\n",
      "2017-06-02   9.17   9.422943   8.921057\n",
      "2017-06-01   9.19   9.380048   8.939952\n",
      "\n",
      "[489 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import talib\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "pro=ts.pro_api()\n",
    "df_whole=pro.daily(ts_code=\"000001.SZ\",start_date=\"20170601\",end_date=\"20190630\")\n",
    "\n",
    "df_whole['trade_date'] = pd.to_datetime(df_whole['trade_date'],format='%Y%m%d')\n",
    "df_whole.set_index('trade_date',inplace=True)\n",
    "print(df_whole.head())\n",
    "\n",
    "\n",
    "symbol=\"000001.SZ\"\n",
    "df=df_whole[\"close\"]\n",
    "# calculate Simple Moving Average with 20 days window\n",
    "sma = df.rolling(window=20).mean()\n",
    "# calculate the standar deviation\n",
    "rstd = df.rolling(window=20).std()\n",
    "upper_band = sma + 2 * rstd\n",
    "upper_band=upper_band.to_frame()\n",
    "upper_band=upper_band.rename({'close': 'upper'}, axis=1) \n",
    "lower_band = sma - 2 * rstd\n",
    "lower_band=lower_band.to_frame()\n",
    "lower_band=lower_band.rename({'close': 'lower'}, axis=1) \n",
    "\n",
    "BB_df=pd.concat([df,upper_band, lower_band], axis=1)\n",
    "\n",
    "BB_df.dropna(inplace=True)\n",
    "print(BB_df)\n",
    "\n",
    "ax = BB_df[\"close\"].plot(title='{} Price and BB'.format(symbol),legend=True)\n",
    "ax= BB_df[\"upper\"].plot(legend=True)\n",
    "ax= BB_df[\"lower\"].plot(legend=True)\n",
    "ax.fill_between(BB_df.index, BB_df['lower'], BB_df['upper'], color='#ADCCFF', alpha='0.8')\n",
    "ax.set_xlabel('date')\n",
    "ax.set_ylabel('SMA and BB')\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
